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singam96
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ShadeNet-2-20M

Image-to-Image
PyTorch
ONNX
shadenet
inverse-rendering
image-decomposition
albedo
normal-map
depth-estimation
pbr
material-estimation
flickr8k
Eval Results (legacy)
Model card Files
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ShadeNet-2 20M: weights (EMA), ONNX fp32+int8, app + card

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Files changed (17) hide show
  1. README.md +9 -1
  2. assets/152029243_b3582c36fa_result.png +2 -2
  3. assets/154871781_ae77696b77_result.png +2 -2
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  15. checkpoints/shadenet2.ckpt +2 -2
  16. onnx/model.onnx +1 -1
  17. onnx/model_quantized.onnx +1 -1
README.md CHANGED
@@ -136,10 +136,18 @@ Losses: per-map weighted MSE (depth term is **scale-shift-invariant** after leas
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  |---|---|---|---|---|---|
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  | last.ckpt | 0.2467 | 0.2912 | 0.0954 | 0.2541 | 0.0690 |
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  | best.ckpt (raw) | 0.2464 | 0.2879 | 0.0957 | 0.2561 | 0.0688 |
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- | **best.ckpt (EMA, shipped)** | **0.2411** | **0.2758** | **0.0925** | 0.2542 | 0.0730 |
 
 
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  Full 807-image val split. Depth is the best-learned map; normals the hardest.
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  ## Limitations
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  - Depth is **relative**, not metric — don't read meters off it.
 
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  |---|---|---|---|---|---|
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  | last.ckpt | 0.2467 | 0.2912 | 0.0954 | 0.2541 | 0.0690 |
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  | best.ckpt (raw) | 0.2464 | 0.2879 | 0.0957 | 0.2561 | 0.0688 |
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+ | best.ckpt (EMA) | 0.2411 | 0.2758 | 0.0925 | 0.2542 | 0.0730 |
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+ | **best-calibrated (EMA, shipped)** | **0.2384** | **0.2726** | **0.0920** | **0.2456** | 0.0787 |
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+ | calibrated + 3-pass ensemble | 0.2373 | 0.2729 | 0.0917 | 0.2420 | 0.0791 |
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  Full 807-image val split. Depth is the best-learned map; normals the hardest.
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+ Shipped weights carry a post-hoc Optuna calibration (inference-only, no
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+ retraining): per-layer weight affines on the three parameter groups that a
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+ sensitivity audit showed could matter (`head_out`/`head`/`up4` — everything
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+ else is GroupNorm-invariant), tuned on val-loss. All page outputs use the
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+ calibrated weights with a 3-pass multi-scale median ensemble.
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+
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  ## Limitations
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  - Depth is **relative**, not metric — don't read meters off it.
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